Toward Learning Robust Detectors from Imbalanced Datasets Leveraging Weighted Adversarial Training

Machine learning is an attractive technique in the security field to automate anomaly detection and to detect unknown threats. Most of the real-world training samples to learn with neural networks are imbalanced from the viewpoint of their distribution and importance priority on each class. In particular, datasets for security problems are imbalanced in most cases. Learning from an imbalanced dataset may cause the degradation of a classifier’s performance, especially in the minority but important classes. We thus propose a new robust learning method for imbalanced datasets using adversarial training. Our proposed method leverages adversarial training to expand classification areas of minority classes. Specifically, we design weighted adversarial training, where the perturbation size of adversarial examples is weighted according to the number of samples in each class. We conducted experiments with real-world datasets, and the results demonstrate that our proposed method increases classification performance in both binary and multiclass classifications. Namely, our proposed method makes classifiers more robust even if the dataset is imbalanced, which is useful for us to apply machine learning to security tasks.